arXiv AI By Xinbo Wang, Shian Jia, Ziyang Huang, Jing Cao, Mingli Song

Beyond the Monolithic Scheduler: Lightweight Adaptive Policy Switcher for Process Scheduling Optimization

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arXiv Machine Learning
Jul 7

AdaptiveSD A Stability-Aware, Runtime-Adaptive Speculative Decoding Framework with Multi-Policy Orchestration for CPU-Constrained LLM Inference

arXiv:2607. 03876v1 Announce Type: new Abstract: With the rise of small quantized GGUF-based language models and their increasing use for on-device inference tasks, we have seen the growing need for an approach capable of reliably delivering these models at scale even under severe memory bandwidth constraints such as those imposed by pure CPU implementations.

By Sadra Saremi
arXiv Machine Learning
Aug 31

DART-FL: Burst-Aware Multitask Federated Learning under Dynamic Inference Demand at the Edge

DART-FL is a multitask federated learning framework designed for edge devices that must balance online inference and model training under limited resources. It dynamically allocates resources between inference and training based on current inference backlog and service capacity, then distributes remaining training capacity among tasks using a queue‑aware scheduler that adjusts loss weights. Experiments on image classification datasets with synthetic and real workloads show that DART‑FL adapts to bursty inference demand, improving accuracy for high‑demand tasks while preserving overall multitask performance.

By Yiming Xie, Pinrui Yu, Geng Yuan, Xue Lin, Ningfang Mi